2021/01/31 by Pranay Lohia, Lohia, Pranay
Computer Science · Engineering · Mathematics · Neuroscience · Psychology · Social Sciences · #Adversarial Robustness in Machine Learning #Allotment #Artificial Intelligence (cs.AI) #Artificial intelligence #Class (philosophy) #Computer science #Economics #Engineering #Environmental economics #Ethics and Social Impacts of AI #FOS: Computer and information sciences #Group (periodic table) #Injustice #International economics #Mathematics #Microeconomics #Operations research #Population #Process (computing) #Psychology #Psychology of Moral and Emotional Judgment #Social psychology #Tariff #Work (physics) #cs.AI
paper · pdf · doi:10.48550/arxiv.2102.00417
arxiv created 2021/01/31 · openalex publication_date 2021/01/31 · arxiv updated 2021/02/02 · openalex created_date 2021/02/15 · openalex updated_date 2026/07/28
Previous post-processing bias mitigation algorithms on both group and individual fairness don't work on regression models and datasets with multi-class numerical labels. We propose a priority-based post-processing bias mitigation on both group and individual fairness with the notion that similar individuals should get similar outcomes irrespective of socio-economic factors and more the unfairness, more the injustice. We establish this proposition by a case study on tariff allotment in a smart grid. Our novel framework establishes it by using a user segmentation algorithm to capture the consumption strategy better. This process ensures priority-based fair pricing for group and individual facing the maximum injustice. It upholds the notion of fair tariff allotment to the entire population taken into consideration without modifying the in-built process for tariff calculation. We also validate our method and show superior performance to previous work on a real-world dataset in criminal sentencing.